Hoang Thanh Nguyen

VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication

Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed. To respond to that need, we collected a dataset of realistic scenarios in a multi-camera network environment (VideoWeb) involving multiple persons performing dozens of different repetitive and non-repetitive activities. This chapter describes the details of the dataset. We believe that this VideoWeb Activities dataset is unique and it is one of the most challenging datasets available today. The dataset is publicly available online at http://vwdata.ee.ucr.edu/ along with the data annotation.

VideoWeb: Design of a wireless camera network for real-time monitoring of activities

Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and non-imaging sensors such as acoustics, seismic, vibration, temperature, humidity, etc. The development of emerging video sensor networks poses its own set of unique challenges, including high bandwidth and low latency requirements for real-time processing and control. This paper presents a systematic approach for the design, implementation, and evaluation of a large-scale, software-reconfigurable, wireless camera network, suitable for a variety of practical real-time applications. We take into consideration issues related to the hardware, software, control, architecture, network connectivity, performance evaluation, and data processing strate-gies for the network. We perform multi-objective optimization on settings such as video resolution and compression quality to provide insight into the performance trade-offs when configuring such a network.

Tracking pedestrians with bacterial foraging optimization swarms

Pedestrian tracking is an important problem with many practical applications in fields such as security, animation, and human computer interaction (HCI). In this paper, we introduce a previously-unexplored swarm intelligence approach to multi-object monocular tracking by using Bacterial Foraging Optimization (BFO) swarms to drive a novel part-based pedestrian appearance tracker. We show that tracking a pedestrian by segmenting the body into parts outperforms popular blob-based methods and that using BFO can improve performance over traditional Particle Swarm Optimization and Particle Filter methods.

Design and optimization of the videoweb wireless camera network

Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and nonimaging sensors such as acoustics, seismic, vibration, temperature, humidity. The emerging development of video sensor networks poses its own set of unique challenges, including high-bandwidth and low latency requirements for real-time processing and control. This paper presents a systematic approach by detailing the design, implementation, and evaluation of a large-scale wireless camera network, suitable for a variety of practical real-time applications. We take into consideration issues related to hardware, software, control, architecture, network connectivity, performance evaluation, and data-processing strategies for the network. We also perform multiobjective optimization on settings such as video resolution and compression quality to provide insight into the performance trade-offs when configuring such a network and present lessons learned in the building and daily usage of the network.

Multi-object tracking in non-stationary video using bacterial foraging swarms

One of the key problems in the field of image processing is object tracking in video. Multiple objects, occlusion, and non-stationary video are some of the challenges that one may face in developing an effective approach. A less-studied approach considers swarm intelligence. This paper presents a new and improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from a moving camera. A comparison with various algorithms is provided.

Tracking multiple objects in non-stationary video

One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the challenges that one may face in developing an effective approach for tracking. While there are numerous algorithms and approaches to the tracking problem with their own shortcomings, a less-studied approach considers swarm intelligence. Swarm intelligence algorithms are often suited for optimization problems, but require advancements for tracking objects in video. This paper presents an improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from both fixed and moving cameras. A comparison with various algorithms is provided.

Real-Time Pedestrian Tracking with Bacterial Foraging Optimization

In this paper, we present swarm intelligence algorithms for pedestrian tracking. In particular, we present a modified Bacterial Foraging Optimization (BFO) algorithm and show that it outperforms PSO in a number of important metrics for pedestrian tracking. In our experiments, we show that BFOs search strategy is inherently more efficient than PSO under a range of variables with regard to the number of fitness evaluations which need to be performed when tracking. We also compare the proposed BFO approach with other commonly-used trackers and present experimental results on the CAVIAR dataset as well as on the difficult PETS2010 S2.L3 crowd video.

VideoWeb: Optimizing a Wireless Camera Network for Real-time Surveillance

Wireless camera networks provide a unique opportunity for collaborative surveillance. Performance evaluation and optimization of camera networks, however, has seldom been addressed. This chapter fills this gap by detailing an approach by which individual cameras and a whole network of cameras can be simultaneously optimized in terms of Pareto-efficiency using multi-objective optimization of performance metrics. Experiments are performed on a set of 37 wireless cameras from a testbed built from the ground up at the University of California at Riverside.